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Entorhinal Cortex

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Introduction to part two of the special issue on computational models of hippocampus and related structures.

Hippocampus
Extensive computational modeling has focused on the hippocampal formation and related cortical structures. This introduction describes the topics addressed by individual articles in part two of this special issue of the journal Hippocampus on the top...

Biomimetic FPGA-based spatial navigation model with grid cells and place cells.

Neural networks : the official journal of the International Neural Network Society
The mammalian spatial navigation system is characterized by an initial divergence of internal representations, with disparate classes of neurons responding to distinct features including location, speed, borders and head direction; an ensuing converg...

Oscillation-Driven Memory Encoding, Maintenance, and Recall in an Entorhinal-Hippocampal Circuit Model.

Cerebral cortex (New York, N.Y. : 1991)
During the execution of working memory tasks, task-relevant information is processed by local circuits across multiple brain regions. How this multiarea computation is conducted by the brain remains largely unknown. To explore such mechanisms in spat...

Flexible modulation of sequence generation in the entorhinal-hippocampal system.

Nature neuroscience
Exploration, consolidation and planning depend on the generation of sequential state representations. However, these algorithms require disparate forms of sampling dynamics for optimal performance. We theorize how the brain should adapt internally ge...

Hippocampal formation-inspired probabilistic generative model.

Neural networks : the official journal of the International Neural Network Society
In building artificial intelligence (AI) agents, referring to how brains function in real environments can accelerate development by reducing the design space. In this study, we propose a probabilistic generative model (PGM) for navigation in uncerta...

Normalized unitary synaptic signaling of the hippocampus and entorhinal cortex predicted by deep learning of experimental recordings.

Communications biology
Biologically realistic computer simulations of neuronal circuits require systematic data-driven modeling of neuron type-specific synaptic activity. However, limited experimental yield, heterogeneous recordings conditions, and ambiguous neuronal ident...

NeuroAI: If grid cells are the answer, is path integration the question?

Current biology : CB
Spatially modulated neurons known as grid cells are thought to play an important role in spatial cognition. A new study has found that units with grid-cell-like properties can emerge within artificial neural networks trained to path integrate, and de...

An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI.

Sensors (Basel, Switzerland)
Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients' lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widel...

Determinantal point process attention over grid cell code supports out of distribution generalization.

eLife
Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall ...

Deep Learning-Emerged Grid Cells-Based Bio-Inspired Navigation in Robotics.

Sensors (Basel, Switzerland)
Grid cells in the brain's entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusin...